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The $100 Million Theranos Fundraising Model Could Inspire a New Generation of AI Scams

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Oct 31, 2026

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The $100 Million Theranos Fundraising Model Could Inspire a New Generation of AI Scams

The technology industry has a new reason to revisit one of Silicon Valley's biggest corporate scandals. As artificial intelligence attracts enormous investments and ambitious startup valuations, former Theranos whistleblower Tyler Shultz is warning that the AI boom could create an unusually attractive environment for deception.

His concern is not that artificial intelligence is fundamentally fraudulent. It is that investors may struggle to distinguish genuine technological breakthroughs from convincing demonstrations, repackaged software, and business models built primarily around hype.

That distinction could become one of the most important investment questions of the AI era.

The Theranos Warning Returns at the Peak of the AI Boom

Tyler Shultz, a former Theranos employee, became a central whistleblower after discovering serious problems with the company's blood-testing technology.

Theranos promised to revolutionize medical diagnostics through machines capable of conducting numerous blood tests using tiny samples. The company attracted major investors, secured partnerships that brought its technology into Walgreens stores, and eventually reached a valuation of approximately $10 billion.

But the technology did not perform as promised.

Instead of relying consistently on its own machines, Theranos outsourced a substantial portion of its testing to conventional third-party equipment. Patients received unreliable results, raising serious concerns about the consequences of commercializing unproven medical technology.

The company's collapse became a defining example of how powerful storytelling, influential connections, and investor enthusiasm can overcome technical evidence.

Now, Shultz believes similar conditions could emerge in artificial intelligence.

AI Creates a Different Kind of Verification Problem

In an interview with The San Francisco Standard, Shultz highlighted a fundamental challenge facing investors.

Artificial intelligence is difficult to evaluate from the outside. A product may appear sophisticated during a demonstration without necessarily possessing proprietary technology, meaningful differentiation, or a sustainable business model.

He specifically raised concerns about companies that simply package existing AI services, such as ChatGPT, into another interface and present them as groundbreaking technology.

This creates an information gap.

Investors may understand the market opportunity but lack the technical expertise to determine whether a startup has developed something genuinely innovative.

In traditional software businesses, evaluating a product's functionality, infrastructure, and customer adoption can provide useful signals. AI introduces additional complexity because the underlying capabilities may depend on external models, APIs, computing infrastructure, and third-party providers.

A polished interface can conceal a relatively ordinary technical foundation.

The $10 Billion Theranos Valuation Shows the Cost of Hype

Theranos demonstrated how far a compelling business narrative could travel before its underlying technology was properly validated.

The company reached approximately $10 billion in valuation despite failing to deliver the technological breakthrough at the center of its business.

Shultz also criticized the informal investment culture that allowed significant amounts of capital to move based on personal relationships and persuasive presentations.

He argued that the days when someone could have a 30-minute coffee meeting and receive $100,000 should be over.

In his assessment, Elizabeth Holmes effectively scaled that fundraising model to approximately $100 million.

An important detail is that Theranos attracted substantial funding from wealthy individual investors, including members of the DeVos and Walton families, rather than relying exclusively on traditional venture capital firms.

The company also withheld scientific information and evidence about its machines from potential investors, citing trade-secret protection.

That approach discouraged some established Silicon Valley venture capitalists.

The lesson is straightforward: investment confidence should never replace independent technical verification.

AI Startups Can Sell the Appearance of Innovation

The AI market makes this problem particularly relevant because the distance between building an impressive product demonstration and creating a defensible technology business can be surprisingly large.

Consider three different types of AI companies.

The first develops proprietary models, infrastructure, or specialized systems that solve difficult technical problems.

The second builds applications on top of existing AI models, adding workflow automation, industry-specific knowledge, integrations, and customer experience.

The third offers little differentiation beyond connecting an existing model to a basic interface while marketing the result as a revolutionary platform.

These businesses are not equally valuable.

However, their presentations can look remarkably similar during an investor meeting.

A company does not necessarily need to develop its own foundation model to build a valuable AI business. Application-layer companies can create substantial value through distribution, specialized workflows, customer relationships, and integration.

The real problem begins when a business exaggerates what it owns, what it has developed, or what its technology can actually accomplish.

AI Business Models Depend on More Than Impressive Demonstrations

The strongest AI businesses will need to demonstrate that their technology creates measurable economic value.

For enterprise customers, that means reducing operating costs, increasing productivity, improving decision-making, or generating revenue.

A chatbot that answers questions is relatively easy to demonstrate. A system that reliably manages complex business processes, integrates with existing software, protects sensitive information, and produces measurable financial results is considerably harder to build.

This distinction matters for investors evaluating recurring revenue and long-term competitive advantages.

An AI startup relying entirely on third-party models may face changing API prices, model upgrades, platform restrictions, and competition from larger providers.

Its competitive advantage must therefore come from something beyond access to a popular model.

That could include proprietary data, distribution, specialized workflows, customer trust, or operational expertise.

Without these advantages, a company risks becoming interchangeable.

The Return of Elizabeth Holmes Raises Additional Concerns

The documentary You Can See Everything, directed by Nathan Fielder and Lance Oppenheim, has brought Elizabeth Holmes back into public discussion.

Rather than presenting Holmes exclusively as either a criminal mastermind or a victim, the documentary examines how she continues to attract sympathy and public attention.

Shultz believes that continued attention may serve Holmes' interests.

The documentary also shows Holmes and her partner, Billy Evans, working on Haemanthus, a diagnostics venture described as Theranos 2.0. The company has reportedly raised approximately $20 million.

For Shultz, this raises concerns about what might happen next.

He believes Holmes is preparing for another chapter, although he does not know what direction it will take.

The broader business lesson is that corporate accountability should not disappear simply because public attention moves elsewhere.

The Competitive Advantage Will Be Trust and Execution

The AI industry is increasingly competitive. Access to powerful models alone is unlikely to guarantee long-term success.

Large technology companies have infrastructure, distribution, capital, and established customer relationships. Smaller startups can compete through specialization, faster execution, and a deeper understanding of particular industries.

However, both face the same fundamental requirement: delivering what they promise.

For investors, evaluating an AI company should involve examining its actual technology, customer retention, revenue quality, operating costs, and dependence on external providers.

A successful demonstration is only an initial signal.

Real customer adoption and repeatable economic value provide stronger evidence of a sustainable business.

Small Businesses Should Avoid Buying the AI Hype

Small and medium-sized businesses face a similar challenge, although their financial exposure is different.

They may not invest millions in startups, but they regularly purchase AI subscriptions, automation platforms, consulting services, and software promising dramatic productivity improvements.

The same verification principles apply.

Before purchasing an AI solution, businesses should:

Test the product using real operational tasks rather than promotional demonstrations.
Verify whether the provider develops its own technology or relies on external AI models.
Calculate the total cost of subscriptions, API usage, integration, and maintenance.
Check data protection, access permissions, and ownership arrangements.
Measure actual time savings, productivity improvements, and revenue impact.
Avoid long-term commitments before the product demonstrates measurable value.

For an SME, an AI tool that saves five hours of administrative work each week may be more valuable than an expensive platform promising to transform the entire organization.

The objective should be business performance, not technological prestige.

The Next Theranos Could Look Like an AI Success Story

The most important warning from Shultz is not that every ambitious AI startup should be treated with suspicion.

It is that artificial intelligence makes it easier to create convincing appearances while making genuine technical differentiation harder to evaluate.

That combination creates opportunities for innovation and opportunities for fraud.

Theranos showed what happens when investors trust a founder's narrative more than scientific evidence. The AI industry now faces a similar test, although the underlying technologies and commercial opportunities are very different.

Capital will continue flowing toward artificial intelligence. Some companies will build extraordinary businesses, while others will struggle to justify their valuations.

The winners will not simply be those with the most impressive demonstrations or the loudest promises.

They will be the companies that can prove their technology works, demonstrate sustainable economics, and earn customer trust over time.

In the AI economy, credibility may become one of the most valuable competitive advantages of all.